Biomedical subjects

Paola Gramatica

Publications and source records attributed to Paola Gramatica.

17 recordsLinked to original sources

PubMed2006

Correlations and complementarities in data and methods through Principal Components Analysis (PCA) applied to the results of the SPIn-Eco Project.

This paper demonstrates how the results from different methods can be interpreted on the basis of a statistical approach that can help find new hints in the evaluation of sustainability at the territorial level. The SPIn-Eco Project for the Province of Siena (Italy) is an example of an environmental sustainability assessment of an area using methods that are suitable for a large system: Ecological Footprint, Greenhouse Gas Inventory, Extended Exergy Analysis, Emergy Evaluation, and Remote Sensing. The calculation of many indicators, derived from these methods, has prompted us to use a statistical method (Principal Components Analysis, PCA) to understand the degree of similarity/congruence of the indicators (here we have examined 26 of them) and the possibility of recognizing patterns or clusters in the description of the 36 municipalities that compose the Province of Siena. Among the results, unexpectedly, emergy flow and the Ecological Footprint resulted as being completely uncorrelated, apparently due to the importance that the non-renewable part of the emergy holds in the evaluation. The municipalities of the province are considerably spread out over the graphs, even though that of Siena is quite far from the rest along the first dimension. In addition, we were able to distinguish between more homogeneous districts (sets of municipalities), such as Val di Merse and Val d'Orcia, and very diverse ones, such as Val d'Elsa and Val di Chiana.

Ecosystem↗

PubMed2006

Accumulation of persistent organic pollutants in canopies of different forest types: role of species composition and altitudinal-temperature gradient.

Leaves from the dominant tree species in three different alpine forests were sampled along an altitudinal gradient and analyzed for HCB, alpha- and gamma-HCH, and PCBs. The mean canopy concentration was calculated, considering the relative abundance of each species in the respective forest. Compound fractionation occurred in the vegetation along the altitudinal/temperature gradient. Results were compared with air concentrations and in-field plant/air partition coefficients (K(PA)) were calculated for each species; this showed differences between broadleaves and needles. The mean canopy/air partition coefficient (K(CA)) was also calculated by averaging results from single species. The variability of persistent organic pollutants distribution in canopies is discussed considering two main factors, the altitudinal/temperature gradient and the species composition. The latter is responsible for most of the concentration variability of the more volatile compounds. A model to calculate dry gaseous deposition to different forest canopies is presented.

Air Pollutants↗

PubMed2006

Statistical external validation and consensus modeling: a QSPR case study for Koc prediction.

The soil sorption partition coefficient (log K(oc)) of a heterogeneous set of 643 organic non-ionic compounds, with a range of more than 6 log units, is predicted by a statistically validated QSAR modeling approach. The applied multiple linear regression (ordinary least squares, OLS) is based on a variety of theoretical molecular descriptors selected by the genetic algorithms-variable subset selection (GA-VSS) procedure. The models were validated for predictivity by different internal and external validation approaches. For external validation we applied self organizing maps (SOM) to split the original data set: the best four-dimensional model, developed on a reduced training set of 93 chemicals, has a predictivity of 78% when applied on 550 validation chemicals (prediction set). The selected molecular descriptors, which could be interpreted through their mechanistic meaning, were compared with the more common physico-chemical descriptors log K(ow) and log S(w). The chemical applicability domain of each model was verified by the leverage approach in order to propose only reliable data. The best predicted data were obtained by consensus modeling from 10 different models in the genetic algorithm model population.

Algorithms↗

PubMed2006

Comparison between 5,10,15,20-tetraaryl- and 5,15-diarylporphyrins as photosensitizers: synthesis, photodynamic activity, and quantitative structure-activity relationship modeling.

The synthesis of a panel of seven nonsymmetric 5,10,15,20-tetraarylporphyrins, 13 symmetric and nonsymmetric 5,15-diarylporphyrins, and one 5,15-diarylchlorin is described. In vitro photodynamic activities on HCT116 human colon adenocarcinoma cells were evaluated by standard cytotoxicity assays. A predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, of a series of 34 tetrapyrrolic photosensitizers (PSs), including the 24 compounds synthesized in this work, was developed to describe the relationship between structural features and photodynamic activity. The present study demonstrates that structural features significantly influence the photodynamic activity of tetrapyrrolic derivatives: diaryl compounds were more active with respect to the tetraarylporphyrins, and among the diaryl derivatives, hydroxy-substituted compounds were more effective than the corresponding methoxy-substituted ones. Furthermore, three monoarylporphyrins, isolated as byproducts during diarylporphyrin synthesis, were considered for both photodynamic and QSAR studies; surprisingly they were found to be particularly active photosensitizers.

Cell Line, Tumor↗

PubMed2006

Quantitative structure-activity relationship modeling of polycyclic aromatic hydrocarbon mutagenicity by classification methods based on holistic theoretical molecular descriptors.

Various polycyclic aromatic hydrocarbons (PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A homogeneous set of mutagenicity data (TA98 and TA100,+S9) for 32 benzocyclopentaphenanthrenes/chrysenes was modeled by the quantitative structure-activity relationship classification methods k-nearest neighbor and classification and regression tree, using theoretical holistic molecular descriptors. Genetic algorithm provided the selection of the best subset of variables for modeling mutagenicity. The models were validated by leave-one-out and leave-50%-out approaches and have good performance, with sensitivity and specificity ranges of 90-100%. Mutagenicity assessment for these PAHs requires only a few theoretical descriptors of their molecular structure.

Environmental Pollutants↗

PubMed2006

QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles.

A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

Algorithms↗

PubMed2006

Analysis of mosses and soils for quantifying heavy metal concentrations in Sicily: a multivariate and spatial analytical approach.

BACKGROUND: The use of vegetal organisms as indicators of contamination of the environment is partially replacing traditional monitoring techniques. Amongst the vegetal organisms available, mosses appear to be good bioindicators and are used for monitoring anthropogenic and natural fall-out on soils. This study has two objectives: the evaluation of the concentrations of heavy metals in soils and mosses of the Sicily Region, in Italy and the identification of the origin of fall-out of heavy metals. METHODS: Mosses and the surface soil were sampled at 28 sites, only the youngest segments of Hylocomium splendens and Hypnum cupressiforme, corresponding to the plant tissues produced during the last 3 years, were taken. The elements Cd, Cu, Ni, Pb and Zn were analysed by ICP-MS and Hg by AAS. Statistical analysis was by PCA and spatial representation by GIS. RESULTS AND DISCUSSION: In the mosses sampled in Sicily, the highest concentrations of Cd were found around the cities of Palermo and Messina. The highest concentrations of Hg were recorded in the northern part of the island between Trapani and Messina, similar to the distribution of Cu. Different areas with the highest concentrations of Ni were found near the south coast, in the vicinity of Palermo and around the Volcano Etna. The highest concentrations of Pb were found in the south-west coast near Agrigento, where important chemical plants and petroleum refineries are located. Except for a few locations, Zn fall-out was found to be evenly distributed throughout Sicily. CONCLUSION: The sites where the concentrations of heavy metals cause greatest concern have been revealed by the PCA analysis and portrayed using GIS. Also of some concern is the diffuse and anthropogenic origin of Hg and Cd. The combined approach of using soil and mosses, together with pedological interpretation and application of multivariate statistical techniques has provided valuable insight into the environmental aspects of heavy metal deposition in a region of southern Europe. RECOMMENDATIONS AND OUTLOOK: Further insight into the deposition of heavy metals will require more detailed sampling of soils and mosses in both new and previous study areas. This needs to be complemented by detailed pedological investigations in the study areas. Future research programmes will address these issues.

Air Pollution↗

PubMed2005

Ranking of aquatic toxicity of esters modelled by QSAR.

Alternative methods like predictions based on Quantitative Structure-Activity Relationships (QSARs) are now accepted to fill data gaps and define priority lists for more expensive and time consuming assessments. A heterogeneous data set of 74 esters was studied for their aquatic toxicity, and available experimental toxicity data on algae, Daphnia and fish were used to develop statistically validated QSAR models, obtained using multiple linear regression (MLR) by the OLS (Ordinary Least Squares) method and GA-VSS (Variable Subset Selection by Genetic Algorithms) to predict missing values. An ESter Aquatic Toxicity INdex (ESATIN) was then obtained by combining, by PCA, experimental and predicted toxicity data, from which model outliers and esters highly influential due to their structure had been eliminated. Finally this integrated aquatic toxicity index, defined by the PC1 score, was modelled using only a few theoretical molecular descriptors. This last QSAR model, statistically validated for its predictive power, could be proposed as a preliminary evaluative method for screening/prioritising esters according to their integrated aquatic toxicity, just starting from their molecular structure.

Esters↗

PubMed2004

Screening the leaching tendency of pesticides applied in the Amu Darya Basin (Uzbekistan).

The Amu Darya River, one of the most important water resources for Uzbekistan and Turkmenistan, was declared a World Disaster Zone in 1991. The great increase in irrigation and the use of pesticides has led to both a lack of water and drinking water contamination. The aim of the present study, part of an EU project on water management guidelines, was to evaluate the leachability of 71 organic pesticides commonly employed in the area, and to assess compounds that could potentially contaminate the river and impair drinking water quality. A multivariate approach is proposed for the pesticide screening, condensing information from different environmental partition indexes (GUS, "modified LEACH", LIN) into a single ranking, the Global Leachability Index (GLI). For a selected data set in water medium this super-index identifies three classes with a risk potential for pesticide leachability, and allows the selection of a small number of chemicals for an analytical survey.

Environmental Monitoring↗

PubMed2004

Joint algal toxicity of phenylurea herbicides is equally predictable by concentration addition and independent action.

Photosynthesis-inhibiting phenylurea derivatives, such as diuron, are widely used as herbicides. Diuron concentrations clearly exceeding the predicted-no-effect concentration have been regularly measured in European freshwater systems. The frequently observed exposure to mixtures of phenylureas additionally increases the hazard to aquatic primary producers. Fluctuating numbers and concentrations of individual toxicants make experimental testing of every potential mixture unfeasible. Thus, predictive approaches to the mixture hazard assessment are needed. For this purpose, two concepts are at hand, both of which make use of known toxicities of the individual components but are based on opposite mechanistic suppositions: Concentration addition is based on the idea of similar mechanisms of action, whereas independent action assumes dissimilarly acting mixture components. On the basis of pharmacological reasoning, it was therefore anticipated that the joint algal toxicity of phenylurea mixtures would be predictable by concentration addition. Indeed, we could demonstrate a high predictive power of concentration addition for these combinations. Surprisingly, however, the opposite concept of independent action proved to be equally valid, because both concepts predicted virtually identical mixture toxicities. This exceptional case has previously been derived from theoretical considerations. Now, the tested phenylurea mixtures serve as an example for the practical relevance of this situation for multicomponent mixtures.

Chlorophyta↗

PubMed2003

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Environmental Exposure↗

PubMed2002

Screening of pesticides for environmental partitioning tendency.

The partitioning tendency of chemicals, in this study pesticides in particular, into different environmental compartments depends mainly on the concurrent relevance of the physico-chemical properties of the chemical itself. To rank the pesticides according to their distribution tendencies in the different environmental compartments we propose a multivariate approach: the combination, by principal component analysis, of those physico-chemical properties like organic carbon partition coefficient (Koc), n-octanol/water partition coefficient (Kow), water solubility (Sw), vapour pressure and Henry's law constant (H) that are more relevant to the determination of environmental partitioning. The resultant macrovariables, the PC1 and PC2 scores here named leaching index (LIN) and volatality index (VIN), are proposed as preliminary environmental partitioning indexes in different media. These two indexes are modeled by theoretical molecular descriptors with satisfactory predictive power. Such an approach allows a rapid pre-determination and screening of the environmental distribution of pesticides starting only from the molecular structure of the pesticide, without any a priori knowledge of the physico-chemical properties.

Biological Availability↗

PubMed2002

Mixture toxicity of priority pollutants at no observed effect concentrations (NOECs).

Environmental exposure situations are often characterised by a multitude of heterogeneous chemicals with ambiguous or unknown modes of action present at low concentrations. While multiple exposure is widely acknowledged, arguments are raised that adverse combined effects might not be evoked by mixtures of substances with dissimilar modes of action and being present at only low concentrations. In this study the combined effect of a multiple mixture composed of structurally dissimilar priority pollutants with mostly unknown modes of action has been investigated using an algal biotest. The concentrations of the components in the mixture equalled statistically estimated, individual no observed effect concentrations (NOECs). The observed mixture toxicity was not only clearly higher than expected for any single substance alone, but also well predictable using the concept of independent action.

Atrazine↗

PubMedDate not supplied

Ranking of phenols for abiotic oxidation in aqueous environment: a QSPR approach.

The limited availability and variability of data related to the overall degradation of compounds in the environment is a very relevant issue in studies related to environmental fate and chemical behavior. The studied phenol data set consists of reaction rate constants of different oxidation reactions in surface waters, available either experimentally or, to fill the data gap, from our QSAR models reported herein. A PCA (Principal Component Analysis) model based on these oxidative degradations has been proposed to evaluate the degradability of chemicals. The score of the first Principal Component is modelled by theoretical molecular descriptors to obtain a multiple linear regression (MLR) model with high predictive power, both internally and externally validated. This modeling approach allows a fast and preliminary ranking of phenols according to their tendency to be degraded by oxidants in water, starting only from knowledge of their molecular structure.

Chlorine Compounds↗

PubMedDate not supplied

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies.

In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.

Journal Article↗

PubMedDate not supplied

Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling.

The rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds is predicted by QSAR modeling. The applied Multiple Linear Regression is based on a variety of theoretical molecular descriptors, selected by the Genetic Algorithms-Variable Subset Selection (GA-VSS) procedure. The models were validated for predictivity by both internal and external validation. For the external validation two splitting approaches, D-optimal Experimental Design and Kohonen Artificial Neural Networks (K-ANN), were applied to the original data set to compare the two methodologies. We emphasize that external validation is the only way to establish a reliable QSAR model for predictive purposes. Predicted data by consensus modeling from different models are also proposed.

Journal Article↗

PubMedDate not supplied

Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow).

The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.

Animals↗